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The MBS controller monitors the state information of specific metrics based on network performance and/or quality of service in CNSC-RAN periodically.
Automatic adjustment (or self-tuning) of network parameters is often implemented as an iterative process, where parameters are gradually changed with a certain period (referred to as optimization loop) based on network performance statistics.
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Extensive simulations based on network simulator-2 show the promising performance of semi-TCP over traditional schemes.
The question remains if social networks induce learning directly, as Baldwin et al. (1997) study suggested, or if students built different social networks based on students' performance, which might be the case in Mayer and Puller (2008) study and explains why the GPA's among friends were so homogeneous.
In the paper, we research on the performance of time-triggered Ethernet based on network calculus.
Method assessment is carried out by simulating classical traffic sharing algorithms in a three-dimensional office scenario with femtocell location plans designed based on different network performance criteria.
Given a particular realization of the number of nodes as Φ(B =k, then the k nodes are independently, uniformly distributed in the area B. The PPP has been widely adopted to model the randomness of wireless networks, based on which the network performance can be analyzed according to the Campbell's theorem, the probability generating function (PGFL), and the Slivnyak theorem [22].
Among various structures of nervous network based on lowest performance, highest R (respectively, Rtrain, Rtest, Rvalid, Rall) and lowest epoch were selected.
Figures 5 and 6 prove that: One-step iterative prediction based on CPRBF network has good prediction performance Comparing with RBF network, CPRBF network has better performance on iterative prediction, in terms of convergence and stability.
In the era of global markets, competition is often based on the performance of business networks rather than those of individual firms.
To sum up, the advantages of using genetic algorithms were based on the performance of neural networks on the testing datasets, instead of only on the minimal square error in the modeling datasets.
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